x = eval("{'tf_avg_pool': 0.00037294511795043947, 'tf_batch_normalization': 0.009068225078768354, 'tf_bias_add': 2.7719807624816894e-05, 'tf_conv2d': 1.8048138512371337, 'tf_dense': 0.022782950830459593, 'tf_max_pool': 0.009139229583740234, 'tf_reduce_max': 0.00020250768661499022, 'tf_reduce_mean': 0.0012095274925231934, 'tf_relu': 0.0002573791027069092, 'tf_sigmoid': 7.757029533386231e-05, 'tf_softmax': 5.021092891693115e-05, 'tf_tanh': 0.0002873828887939453, 'torch_avg_pool': 0.0004279860258102417, 'torch_batch_normalization': 0.00906576373577118, 'torch_bias_add': 3.0462646484375e-05, 'torch_conv2d': 1.892983758527157, 'torch_dense': 0.0188842889547348, 'torch_max_pool': 0.0039611812114715575, 'torch_reduce_max': 0.00019972240924835205, 'torch_reduce_mean': 0.0012216864347457887, 'torch_relu': 0.00013696370124816894, 'torch_sigmoid': 0.00010594258308410644, 'torch_softmax': 0.00015990183353424073, 'torch_tanh': 0.00025482378005981443}")

group = ['bias_add', 'conv2d', 'batch_normalization',
         'avg_pool', 'max_pool', 'relu',
         'sigmoid', 'softmax', 'tanh',
         'dense', 'reduce_mean', 'reduce_max']

dic = {}

for op in group:
    dic[op] = (x['tf_{0}'.format(op)] + x['torch_{0}'.format(op)]) / 2
print(dic)